Proposing a Comprehensive Meta-model for Technology Acceptance

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 2 Issue: 11 3454 – 3472

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3454 IJRITCC | November 2014, Available @ http://www.ijritcc.org

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Proposing a Comprehensive Meta-model for Technology Acceptance

AdhiSusilo

Universitas Terbuka

Jakarta, Indonesia

David Kaufman Simon Fraser University

Burnaby, Canada

Corresponding author

Dr. David Kaufman

Faculty of Education

Simon Fraser University

8888 University Drive

Burnaby, BC

Canada V5A1S6

Abstract--New technologies appear constantly, offering the promise of greater efficiency and effectiveness for work processes in

all types of organizations. However, not all reach their full potential, either because of employee rejection or less-than optimal

implementation. Studies that examine Information Technology (IT) adoption in business have often used the Technology

Acceptance Model (TAM) to predict IT adoption in a business environment. However, the TAM fails to explain much of the

variance in technology usage. This article examines technology acceptance processes in the light of theories of technology

readiness, technology acceptance, and diffusion of innovation and proposes a comprehensive meta-model to integrate and expand

existing models to explain technology acceptance in a wide range of contexts. With regard to future research, the paper also

recommends attention to a greater breadth of contexts, cultures, and questions related to issues and recommendations for

promoting technology acceptance.

Keywords: technology acceptance, Technology Acceptance Model, diffusion of innovation, technology readiness, Technology

Readiness Index

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Introduction

New technologies appear constantly, offering the promise of

greater efficiency and effectiveness for organizational work

processes. However, not all technologies reach their full

potential, either because of employee rejection or less-than

optimal implementation (Burton-Jones & Hubona 2006).

The potential for financial loss and employee dissatisfaction

is high (Venkatesh 2000); therefore, it is important to fully

explain and manage the factors facilitating new technology

adoption.

Much information and communication technology research

has worked to identify determinants of technology use,

resulting in a number of explanatory theories (Kin &He

2006). Porter &Donthu (2006) identify two broad research

paradigms to explain technology adoption and acceptance.

One focuses on how an individual’s perception of a

technology is affected by its attributes; perception in turn

affects the individual’s use of the specific technology. One

of the most widely used models within this paradigm is the

Technology Acceptance Model (TAM), which focuses on

the attributes of perceived usefulness and perceived ease of

use (Davis, Barozzi & Warshaw 1989, Kin & He


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2006,Porter & Donthu 2006). While the TAM has proved an

enduring and popular explanatory model, it only explains

about 40% of the variance in computer usage, suggesting

that additional factors may help explain IT

acceptance(Legris, Ingham & Collerette 2003,Venkatesh &

Davis 2000).

In contrast, other models use a paradigm that focuses on an

individual’s personality to explain technology adoption

(Porter &Donthu 2006). One example is the Technology

Readiness Index (TRI) (Parasuraman 2000), in which

technology readiness can be viewed as resulting from four

personality dimensions (optimism, innovativeness,

discomfort, and insecurity)that affect people's tendency to

use new technologies. The first two dimensions enable

technology adoption, while the last two are inhibitors.

Technology adoption has not been systematically studied in

some environments, for example, Hayes (2012) notes

thatrelatively few studies examine information technology

(IT) adoption in small businesses. Smaller firms far

outnumber larger ones and contribute significantly to the

economy.and they encounter unique technology issues

including reliance on external IT expertise (Thong, Yap &

Raman 1996). Studies that have examined IT adoption in

small firms all imply that an attitude toward the technology

is important determinant of its successful adoption(Caldeira

& Ward 2003,Murchandani & Motwani

2001,Riemenschneider, Harrison & Mykytyn 2003).

The purpose of this paper is twofold. One goal is to improve

upon our understanding of technology adoption by

proposing a revised model that incorporates elements from

multiple models and from the emerging TA literature. Most

importantly, however, the goal is to offer a model that

ultimately better explains IT adoption in a broad range of

environments, including small business, education, and

other types of organizations.

Literature Review

Technology Acceptance

Teo(2011, p. 1) defines technology acceptance as ―a user’s

willingness to employ technology for the tasks it is designed

to support.‖Much research has been done to understand

individual-level technology awareness, acceptance, and use

(Sia, Lee, Teo & Wei 2001) in various contexts, such as

digital libraries (Hong 2002), and collaboration systems

(Choon-Ling, Hock-Hai, Tan & Kwok-Kee 2004,Sia, Tan &

Wei 2002). In fact, technology acceptance research is one of

the most, if not the most, mature streams in IS research (Sun

& Zhang 2006,Venkatesh, Morris, Davis & Davis 2003). In

most of the acceptance studies, researchers have sought to

identify and understand the forces that shape users’

acceptance so as to influence the design and implementation

process in ways to avoid or minimize resistance or rejection

when users interact with technology. This has given rise to

the identification of core technological and psychological

variables underlying acceptance. From these, acceptance

models have emerged, some extending the theories from

psychology with a focus on the attitude-intention paradigm

in explaining technology usage, and allowing researchers to

predict user acceptance of potential emerging technology

applications.

Ball (2008) provides a useful review of the technology

acceptance literature, highlighting themes common to

multiple models. Sun & Zhang (2006) identify through

systematic analysis three main factors and 10 moderating

factors associated with technology acceptance models in the

literature and develop an integrative model and propositions

associated with each of the factors. According to Sun &

Zhang(summarized in Ball 2008), it appears thatdespite their

considerable empirical validation and confirmation,

technology acceptance models have room for improvement.

Moreover, research studies report inconsistent results,

leading to a need for further research in different

environments(Legris et al. 2003,Sun & Zhang 2006).

Hu, Clark & Ma (2003) suggest that job relevance is

important for technology acceptance. They argue that while

a number of factors influence initial acceptance of

technology, fundamental determinants play a greater role in

continued acceptance. According to Thompson,

Compeau&Higgins (2006), although existing models have

served the technology adoption stream well, they may lead

to a narrow understanding of technology acceptance and

might not serve modern technologies effectively.

Wong,Teo& Sharon (2012) argue that it is crucial to look

further into the contribution of external variables. In this

vein, recent studies have found significant effects

fromperceived enjoyment (Teo & Noyes 2011), facilitating

conditions (Terzis & Economides 2011), social influence

(Moran, Hawkes & El Gayar 2010,Terzis & Economides

2011), and self-efficacy (Chen 2010,Moran et al. 2010).

Theories of Technology Acceptance

The following sections review existing IT acceptance

models in detail as a foundation for a proposed meta-model.

Theory of Reasoned Action.In their Theory of Reasoned

Action,Fishbein & Ajzen (1975) describe how attitudes are

formed by asserting that understanding human behaviors

requires separate evaluation of (1) beliefs, (2) attitudes, (3)

intentions, and (4) behaviors (p. 10). They stress the

importance of experiences to formation of attitudes as well

as belief systems, knowledge, and intention, contributing to


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how individuals accept or reject innovations.They find that

past events, beliefs and experiences all affect the elements

that form attitudes that lead to behaviors. When those

attitudes are understood, behaviors can be predicted in one

or more ways, and when an individual’s predisposition is

established, it is expected that they will or will not perform

the behavior in question (Fishbein & Ajzen 1975, p. 9). The

authors assert that contributors to predispositions and

attitudes are measured in multiple ways; one method of

determining attitudes is through single-question interviews

and surveys that measure likes and dislikes.

The TRA has been successful in predicting and explaining

behaviour in general (Yi & Hwang 2003), and perceived

usefulness and perceived ease of use have proven to be

reliable and valid cognitive dimensions in a range of

contexts (Burton-Jones & Hubona 2006,King & He 2006).

Technology Acceptance Model (TAM). A modification of

the TRA,the Technology Acceptance Model(Davis 1989,

1993; Davis et al. 1989)regards an individual’s behavioral

intention to use information technology as jointly

determined by the perceived usefulness (PU) of the

information technology and the individual’s attitude toward

using it, primarily reflected in perceived ease of use

(PEU).PU refers to "the degree to which a person believes

that using a particular system would enhance his or her job

performance" (Davis 1989, p. 320); and PEU refers to "the

degree to which a person believes that using a particular

system would be free of effort" (Davis 1989, p. 320).

Davis’ studies specify the causal relationships between

system design features, perceived usefulness, perceived ease

of use, attitude toward using, and actual usage behavior

(Davis 1993). His findings indicated that, although

indirectly affecting attitude through its effect on usefulness,

perceived ease of use has a ―fairly small direct effect on

attitude‖ (Davis 1993, p. 482). Perceived usefulness,

however, has a very strong effect on actual use through

attitude. Venkatesh and colleagues have also studied TAM

extensively and evaluated its use as a predictor of user

behavior (Davis & Venkatesh 1996,Venkatesh

1999,Venkatesh & Morris 2000,Venkatesh et al.

2003,Venkatesh, Speier & Morris2002).

TAM is linked to Social Cognitive Theory by its two key

constructs, PU and PEU (Davis & Wiedenbeck 2001). It is

notable in TAM that PEU is used as a measure of process

expectations, while PU is a measure of outcome

expectations after actions. The TAM has been proven as

promising theoretical model among the various models

developed to examine users’ intentions to use computer and

communication technology (Gefen & Straub 1997,Moon &

Kim 2001,Taylor & Todd 1995,Venkatesh 1999,Venkatesh

& Davis 1996).

Although research on TAM has provided insights into

technology usage, it has focused on PEU and PU as the

determinants of usage rather than on other factors affecting

users’ determinants. In addition, TAM suggests that users

will use computer technology if they believe it will result in

positive outcomes. The TAM does not explicitly consider

how users’ capabilities influence their perceived behaviors.

The TAM excludes the social norm (SN) construct from the

TRA because the direct effects of the SN component are

difficult to disentangle from its indirect effects through

attitude (Davis et al. 1989).

Davis et al. (1989) posit that behavioral intention (BI) may

be directly affected not only by attitude, but also by PU.

That is, their model suggests a direct link between the user's

perceived usefulness of the system and his/her intention to

use the system. Third, they include external variables in

their model, items that directly affect PU and PEU. System

features, training, user support consultants, and

documentation are all examples of external variables in the

TAM. Finally, the TAM's PU and PEU are expected to

generalize across other systems and users. TAM has been

used to explain or predict user’s behavioral intentions on a

variety of emerging technologies such as electronic

commerce (Çelik & Veysel 2011,Ha & Stoel 2009), wireless

internet (Kim & Garrison 2009), intranet (Horton, Buck,

Waterson & Clegg 2001), telemedicine technology (Hu,

Chau, Sheng & Tam 1999,Kowitlawakul 2011), internet-

based course management systems(Dasgupta, Granger &

McGarry 2002; Hashim 2008, Landry, Griffeth & Hartman

2006), learning management systems(Al-Busaidi & Al-Shihi

2012,Kamla Ali & Hafedh 2010), smart phones(Sek, Lau,

Teoh, Law & Parumo2010), internet banking (Lai & Li

2005,Pikkarainen, Pikkarainen, Karjalutot & Pahnila2004)

and digital library systems(Park, Roman, Lee & Chung

2009).

TAM has been found to explain between 30% and 40% of

system usage (Burton-Jones & Hubona 2006,Legris et al.

2003), with perceived usefulness often the strongest

determinant in the model (Burton-Jones & Hubona

2006,King & He, 2006,Legris et al. 2003). Godoe and

Johansen (2012) note that since the original TAM was

introduced, the model has undergone numerous adjustments,

with some versions including only perceived usefulness,

perceived ease of use, and actual use of a particular system

(e.g., Adams, Nelson & Todd 1992,Burton-Jones & Hubona

2006,Davis 1989).


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Although the TAM has been tested and validated in many

Western cultures; further validations in different cultures

would further enhance our understanding of the efficiency

and parsimony of the TAM and strengthen the cultural

validity of the TAM (Ball 2008;Teo 2009, 2010; Teo,

Ursavaş & Bahçekapili 2011). Also, several researchers

have recommended further research into the generalizability

of factors associated with technology acceptance and

refinement of acceptance models (Ball 2008,Sun & Zhang

2006,Teo et al. 2011,Thompson et al. 2006). Igbaria (1994)

utilizes both TAM and TRA to study of microcomputer

technology acceptance. Her research concludes that both

individual attitudes and situation variables impact whether

an individual will accept a new technology (Igbaria

1994,Igbaria, Guimaraes & Davis 1995).

Based on an extensive investigation on technology

acceptance factors identified in information system (IS)

studies, Legris et al. (2003) suggest that TAM should be

integrated into a broader model that identifies additional

variables that influence technology acceptance. Davis et al.

(2003) suggest that a model comprised of elements from

both TAM and TRA might provide a more complete view of

the determinants of user acceptance. In an empirical

assessment of the model, Davis et al. find that the combined

model predicted intention better than either model by itself.

Theory of Planned Behavior.Some researchers believe that

technology acceptance is more complex and have

investigated other variables that influence acceptance

(Taylor & Todd 1995,Thompson et al. 2006). TRA and

TAM have strong behavioral elements and predict intention

well, but they are limited in explanatory power and do not

account for other factors that may influence technology

acceptance (Sun & Zhang 2006,Thompson et al.

2006,Venkatesh & Davis 1996). Ajzen (1991) extended the

TRA and developed the Theory of Planned Behavior (TPB)

by empirically investigating the influence of perceived

behavioral control, attitude, and subjective norms on

technology acceptance(Ajzen 1991,Fishbein & Ajzen

1975,Schifter & Ajzen 1985). The sTPBis a well-researched

model that is widely used in predicting and explaining

human behavior across a variety of settings while also

considering the roles of individual and social systems in the

process (Ajzen 1991). TPB identifies three attitudinal

antecedents of behavioral intention. Two reflect the

perceived desirability of performing the behavior: attitude

toward outcomes of the behavior and subjective norm.

Perceived behavioral control also reflects perceptions that

the behavior is personally controllable (Ajzen 1987, 1991).

Ajzen found that the TPB is highly accurate in its

predictions of user’s behavioral intentions, and that people

generally behave in accordance with their intentions.

Taylor and Todd (1995) test a decomposed TPB model that

in some cases provides a better understanding of

relationships than TAM. They use this model to examine

specific antecedents to attitude, subjective norm, and

perceived behavioral control in attempting to make TPB

consistent and generalizable across different settings. There

is evidence that the TPB is a valid model to explain pre-

service teachers’ acceptance of technology, specifically in

terms of their behavioral intention to use technology (Teo &

Tan 2012).

As researchers have used the TAM in their studies, they

have added other factors from the TPB. In their

review,Venkatesh et al. (2003) identify studies linking the

two theories in a separate category. Davis, Bagozzi

&Warshaw (1992) believe that usefulness and enjoyment

mediate the perceived usefulness and ease of use of the

participants. Lim (2003) uses a combination of the TAM

and TPB to study the adoption of negotiation support

systems and finds it to be valid. Combining the TAM and

the TPB, Chau & Hu (2002) do not find that these theories

are effective for studying technology acceptance by

individual professionals in the healthcare setting. But the

integration of TAM and TPB confirms its robustness in

predicting user’ intention to use new technology (Yang,

Liu& Zhou 2012,Yang & Zhou 2011).

Model Combining the Technology Acceptance Model

and Theory of Planned Behavior. To further investigate

the complex relationships between technology acceptance

variables, Taylor & Todd (1995) develop an extension to

TPB, the Decomposed Theory of Planned Behavior

(DTPB), and identify eight additional components to explain

some of the antecedents to the original TPB variables more

fully. DTPB provides a more complete understanding of BI

and provides a better predictive power relative to TAM and

TPB.

Thompson et al. (2006) believe that technology adoption

needs to be approached in a more holistic fashion, and

propose an integrative model that extends DTPB. Thompson

et al. reveal strong influences of personal innovativeness

and self-efficacy. In another study that integrates TAM and

TPB, Chen, Fan &Farn (2007) show that the overall

explanatory power of their research model is high and that it

explains a high proportion of the variance in BI. Chen et al.

suggest that integrating TPB with TAM might provide a

more complete understanding of BI, and recommend further

research into possible moderating factors that may

contribute to BI.


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Motivational Model. The effect of motivation on user

acceptance has developed into a separate model. Both

extrinsic and intrinsic motivations have been found to

impact new technology adoption (Malhotra, Galletta &

Kirsch 2008,Shroff & Vogel 2009,Zhang, Zhao & Tan

2008).

Model of PC Utilization.The Model of PC Utilization

(MPCU) is based primarily on Triandis’ Theory of Human

Behavior (Triandis 1977). The constructs for the model

include social factors and long-term consequences.

Thompson, Higgins &Howell (1991) modify and develop

Triandis’ model for information system contexts and use the

model to predict PC utilization, working to predict usage

behavior rather than intention. Bagchi, Hart, and Peterson

(2004) address one of these constructs in their research on

the impact of national culture on Information Technology

(IT) adoption. Their results show that even after controlling

for national economic and social differences, national

cultural dimensions significantly predict most IT product

adoptions.

Cheung, Chang, & Lai (2000) utilize the MPCU in a study

of World Wide Web users. Their study confirms that

facilitating conditions and social factors should be part of an

acceptance theory. A study of a groupware application by

Li, Lou, Day &Coombs (2004) use the attachment theory to

research individual motivation and intention to use a

technology. The attachment theory seems to be subset of the

MPCU.

Theory of Diffusion of Innovations.Change is measured

and assessed by a method known as diffusion (Banks,

2002). According to Rogers (2003, p. 10), "diffusion is a

process through which a new innovation is communicated

through specific channels over a period of time, among the

members of a social system."Essentially, diffusion theory

describes how and when new ideas are either adopted or

rejected, and how rapidly they are spread through society.

The theory of diffusion of innovations in adoption of

technology includes four main elements that apply to social

systems, organizations, or individuals: (1) innovation, (2)

communication channels, (3) time, and (4) social system.

Diffusion theory has been used for years to determine the

acceptance and spread of any new technology within an

organization, and it is the most relevant in describing the

evaluation, selection, and adoption of any new technology

(Banks 2002,Powell 2008). Rogers’ Innovation Decision

Process theory states that an innovation’s diffusion is a

process that occurs over time through five stages:

Knowledge, Persuasion, Decision, Implementation and

Confirmation. Accordingly, ―the innovation-decision

process is the process through which an individual (or other

decision-making unit) passes from first knowledge of an

innovation to forming an attitude toward the innovation to a

decision to adopt or reject to implementation of the new

idea, and to confirmation of this decision‖ (Rogers 2003, p.

168).

According to Rogers (2003), who uses the terms innovation

and technology interchangeably, people’s attitudes toward a

new technology are a key element in its diffusion. Albirini

(2006) reviews studies on technology diffusion in education,

noting that they have often focused on the first three phases

of the innovation decision process. In cases where

technology has been recently introduced into an educational

system, studies have mainly focused on the first two stages,

that is, on knowledge of an innovation and attitudes about it

(Albirini 2006).

Straub (2009) reviews technology adoption and diffusion

models and concludes that adoption theory focuses not on

the whole but rather the pieces that make up the whole. In

contrast, diffusion theory describes how an innovation

spreads through a population, considering factors such as

time and social pressures.Adoption theory takes a micro-

view, while diffusion theory takes a macro-perspective on

the spread of an innovation across time(Straub 2009).

Adoption and diffusion of new technology have been

studied in many different areas such asmedical and

healthcare (Hikmet, Banerjee& Burns 2012,Sanson-Fisher

2004), sociology (Deffuant, Huet & Amblard 2005,Rogers

2003), education (Hall & Loucks 1978,Pennington 2004),

and computer science (Venkatesh et al. 2003). The results of

adoption theory are measured in terms of behavioral change,

and the predictors of that behavioral change can be

understood through contextual, cognitive, and affective

factors (Straub 2009).

The Theory of Diffusion of Innovations was developed to

study any innovation, not only technological ones

(Venkatesh et al. 2003). Chen, Gillenson &Sherrell

(2002)combine the Innovation Diffusion Theory (IDT) with

the TAM to study consumers and online virtual stores and

find that they are valid theories.

Social Cognitive Theory.Social Cognitive Theory (SCT)

addresses constructs such as self-efficacy, affect, and

anxiety in determining usage behavior (Bandura

1986,Bandura, Adams& Beyer 1977,Venkatesh et al. 2003).

In a study of the relationship between the enjoyment users

get from software and perceived usefulness and ease of use,

Agarwal & Karahanna (2000) find that that the amount of


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―playfulness‖ in the software or skill being studied can

positively influence perceived usefulness and ease of use.

SCT has been used in various studies, including a study

supporting the significance of self-efficacy and outcome

expectations (Compeau, Higgins&Huff1999); research

supporting the impact of computer playfulness and

computer anxiety (Hackbarth, Grover & Yi2003); and

research performed by Yi & Hwang (2003) on internet use

that utilized students and combined SCT and TAM to

determine that enjoyment, learning goal orientation, and

application-specific self-efficacy positively affect use.

Unified Theory of Acceptance and Use of Technology.

Some researchers have worked to integrate constructs from

various models into a single model with the goal of

providing one comprehensive model that would predict

intention more accurately(Sun & Zhang 2006,Venkatesh et

al. 2003). Venkatesh et al.’s (2003, p. 425) Unified Theory

of Acceptance and Use of Technology model (UTAUT)

integrates elements from eight different technology

acceptance models–The Technology Acceptance Model, the

Theory of Planned Behavior, the model combining the

Technology Acceptance Model and the Theory of Planned

Behavior, the Theory of Reasoned Action, the Motivational

Model, the Model of PC Utilization, the Innovation

Diffusion Theory, and the Social Cognitive Theory. They

find that the eight models individually explain between 17

and 53 percent of the variance in user intentions. The

UTAUT investigates four main variables and four

moderating variables to determine their influence on

technology acceptance. Tests using UTAUT produce higher

percentages than the other models, indicating that it may be

a more accurate model for predicting technology

acceptance. Venkatesh et al. (2003) recommend further

research to identify additional constructs that will improve

the ability to predict user’s intention and behavior.

Technology Readiness.Based on the literature and

extensive qualitative research on customer reactions to

technology, Parasuraman (2000) proposes the construct of

technology readiness (TR). Attitude formation and

characteristics of adopters are intrinsic components of TR

(Rogers 2003). There are also measurable traits of TR

among individuals that serve as indicators regarding how

they adopt innovations and their rate and methods of

adoption (Rogers 2003). Parasuraman & Colby (2001)

describe the term Technology Readiness (TR) as individual

beliefs and behaviors that transpire when new technologies

are introduced in the workplace, school, or home. In other

words, TR is the ―people’s propensity to embrace and use

new technologies for accomplishing goals in home life and

at work‖ (Parasuraman & Colby 2001, p. 18). These

characteristics apply to all individuals engaged with

technologies of all types. Parasuraman&Colby identified

four primary elements of TR that should be considered

when introducing new technologies to consumers. Those

elements are optimism, innovativeness, insecurity, and

discomfort. TR refers to the decision-making process that

individuals engaged in regarding use of technology that is

unique and different from the decision process used for non-

technology decisions. The TR theory is relevant to

understanding the decision-making process of individuals—

including business professionals, educators and learners,

who are confronted with responding positively or negatively

to new technologies (Myers 2010).

Parasuraman & Colby (2001) find in qualitative analysis

that there are positive and negative feelings expressed in

response to technology. They find that TR is more complex

than the categories outlined by Rogers (2003) of innovator,

early adopter, early majority, majority and laggard; and that

the concept of TR included a range of feelings and emotions

of high, medium and low readiness. Parasuraman&Colby

describe emotional reactions to technology as experiences,

attitudes, beliefs and question whether most individuals

actually seek technology or instead need coaxing to accept

technologies being introduced by outside sources. They

produce a continuum to illustrate their analysis of high,

medium and low levels of TR that can be measured on a

scale where resistance to technology is low and receptivity

to technology is high (Parasuraman & Colby 2001).

In addition to measuring responses as high, medium, and

low, Parasuraman & Colby (2001) classify TR into four

distinct domains: optimism, innovativeness, insecurity and

discomfort. They believe that optimism and innovativeness

contribute to an individual’s TR, while discomfort and

insecurity inhibit TR. They refer to contributors and

inhibitors as ―drivers‖ of behaviors (Parasuraman & Colby,

2001).Parasuraman (2000)presents a 36-item technology

readiness index (TRI) scale that measures an individual’s

propensity to embrace and use new technologies.

Parasuraman &Colby (2001)argue that there are five groups

of individuals who reacted to technology in positive and

negative ways. The descriptions used to describe the

personalities are explorers, pioneers, skeptics, paranoids and

laggards, and each personality type required different types

of interventions for promotion of their TR. The categories

are similar to categories of technology adopters listed by

Rogers (2003), but vary somewhat in the characteristics

described. Both sets of researchers include laggards as the

slowest group to accept technology.


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Extended Technology Acceptance Model (TAM 2 and

TAM 3).Venkatesh & Davis (2000) propose an extension of

TAM—TAM2—by identifying and theorizing about the

general determinants of perceived usefulness—that is,

subjective norm, image, job relevance, output quality, result

demonstrability, and perceived ease of use—and two

moderators—that is, experience and voluntariness. The first

two determinants fall into the category of social influence

and the remaining determinants are system characteristics.

TAM2 presents two theoretical processes—social influence

and cognitive instrumental processes—to explain the effects

of the various determinants on perceived usefulness and

behavioral intention. In TAM2, subjective norm and image

are the two determinants of perceived usefulness that

represent the social influence processes. Both social

influence processes (subjective norm, voluntariness, and

image) and cognitive instrumental processes (job relevance,

output quality, result demonstrability, and perceived ease of

use) significantly influenced user acceptance (Venkatesh &

Davis 2000). In addition, TAM2 theorizes that three social

influence mechanisms—compliance, internalization, and

identification—play a role in understanding the social

influence processes (Venkatesh & Bala 2008).

In TAM 3, Venkatesh &Bala (2008) identify disparities

between large investments in IT and the potential for non-

use or low acceptance levels among employees. They

determine that there is some relevance among employees at

companies regarding perceived usefulness, job relevance,

output equality, results and perceived ease of use, however

when those factors are not present employees may not

engage the technology and IT investments could be wasted

(Venkatesh & Bala 2008). In other words, Venkatesh &

Bala (2008)find that major capital investments in

technology could be lost if there is sufficient attention to

ease of use and perceived usefulness. Venkatesh &Bala

(2008)find that individual experience with technology and a

feeling of voluntariness rather than compulsory

requirements are factors that influenced employee

acceptance of new technologies. Findings by

Venkatesh&Bala could be useful for administrator

introducing new technologies who need to determine if non-

use or low-use among user could result in costly delays or

rejectionof technologies.

Findings in TAM 3 indicate that low adoption and

underutilization of technology are in conflict with large

investments in IT and expected increases in productivity.

Venkatesh & Bala (2008) suggest that managers need

assistance with determining the elements of perceived

usefulness and ease of use that addressed individual

differences, system characteristics, social influences and

facilitating conditions.

This literature review builds upon and contributes

toknowledge about what motivates people to accept

technology in general, based largely on the Technology

Acceptance Model and models of technology readiness and

diffusion of innovation. The application domains for TAM

and its many extensions and refinements have broadened out

in several directions. The influence of key constructs

identified in the technology acceptance literaturehas been

presented. However, comprehensive understanding of

technology acceptance among individuals still remains an

issue.

Additional Elements Affecting Technology Acceptance

Recent studies have continued to use the TAM to study IT

adoption in a business setting (Chatzoglou, Vraimaki,

Diamantidis & Sarigiannidis 2010,Dembla, Palvia &

Krishnan 2007).) For example, in their study of web-enabled

transaction processing by small businesses,Dembla et al.

(2007) find that consistent with the TAM, perceived

usefulness is a major determinant of adoption. Further, in

their study of small and medium-sized businesses in Greece,

Chatzoglou et al. (2010) find that perceived usefulness as

well as perceived ease of use are important determinants of

computer acceptance. However, despite its potential, the

TAM still only explains at most about 40% of the variance

in computer usage, suggesting that the current model does

not include significant factors (Legris et al., 2003). Thus, in

the spirit of developing our ability to explain technology

adoption in a society, it makes sense to consider additional

constructs.

Additional Elements: Technology Readiness.

Parasuraman & Colby (2001) believe that TR varies from

individual to individual; a person’s method of adoption will

depend on their nature and personality. The authors also

believe that TR is multifaceted and a blend of different

beliefs, customs and culture. TR is more than just the

tendency to be an innovator or early adopter—it describes a

person referred to by Rogers (2003) as venturesome

(Parasuraman & Colby 2001). TR can also predict

consumer behavior, the adoption rate of new technologies

and explain the manner in which the technology is used,

including association of a degree of satisfaction with

technology and the kind of support required (Parasuraman &

Colby 2001). In contrast, as a combined measure technology

readiness does not predict intention or behaviour, but merely

provides a measure of how ready a market is to adopt

technologies (Rose & Fogarty 2010).

Wejnert (2002) finds that there are consequences when

technologies are introduced without proper

planning.Wejnert observes that there is insufficient research


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_______________________________________________________________________________________

that cross-connects information and analyzes the impact of

innovation on interactions among professionals,

organizations and government. In addition to the public and

private impact of innovations, Wejnert expresses concerns

that diffusion of innovations can affect societies and

governments and result in historic transformations. She

indicates that little regard is provided to conducting costs

benefit analysis regarding the impact of introducing

innovations in the public sector. Her concerns are relevant to

the planning process of the introduction of new technologies

into the public and business sectors. Wejnert suggests there

are differencesbetween diffusing innovations in the private

sector and in the public sector. She argues that when

innovations are spread to society and new ideas or tools are

spread throughout the society, new philosophies or ways of

thinking can change entire cultures (Wejnert 2002).

Additional Elements: Technology Acceptance. Fishbein

&Ajzen’s (1975) research is applied to the predictability of

attitude formation in general and not just attitudes regarding

TA. In contrast, Mick &Fournier (1998) analyze attitudes

towards technology and determine that there are conflicting

attitudes about technology that can co-exist simultaneously.

They argue that there are technology paradoxes in many

situations that describe co-existing attraction and avoidance

responses to technology. They conclude that thereare

significant apprehensions and concerns about technology

and ownership of technological products that foster complex

and conflicting feelings in individuals, sometimes resulting

in negative reactions to innovation. These paradoxes are

described in Table 1.

Table 1.The eight paradoxes (Mick & Fournier 1998, p. 126)

Paradoxes Description

Control/Chaos Technology can facilitate regulation or order, and technology can lead to upheaval or

disorder

Freedom/enslavement Technology can facilitate independence or fewer restrictions, and technology can lead

to dependence or more restrictions

New/obsolete New technologies provide the user with the most recently developed benefits of

scientific knowledge or new technologies that are already or soon to be outmoded as

they reach the marketplace

Competence/incompetence Technology can facilitate feelings of intelligence or efficacy and technology can lead

to feelings of ignorance or ineptitude

Efficiency/inefficiency Technology can facilitate less effort or time spent in certain activities and technology

can lead to more effort or time in other activities

Fulfills/creates needs Technology can facilitate the fulfillment of needs and technology can create new

needs

Assimilation/isolation Technology can facilitate human togetherness and technology can lead to human

separation

Engaging/disengaging Technology can facilitate involvement, flow or activity and technology can lead to

disconnection, disruption or passivity

Mick & Fournier’s (1998) examples show how potential

users/consumers react with conflicting emotions about

technological household and office products, generating

tension between fulfilling needs and creating new needs by

new technologies. Consumers and users of technology use a

range of strategic behaviors to cope with these technology

paradoxes, including ―avoidance of technology; delays of

use of new technologies; becoming acquainted with new

technologies through other individuals; making extra efforts

to understand and use technologies through partnering; and

thoroughly learning new technology operations, strengths

and weaknesses‖ (Mick & Fournier 1998, p. 140). They

also determine that coping mechanisms are moderated by

product, situation, and person factors over time and those

mediators and moderators affect coping strategies. Based on

their analysis, individuals are acutely aware of the presence

of technologies in their lives and of the need to deal with

them in their homes and workplaces, but they do not

automatically accept technologies as ubiquitous and

essential to their lives. Mick & Fournier (1998) recognize


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_______________________________________________________________________________________

that paradoxes in TA contributed to the complexity of

adoption of technology in societies.

Additional Elements: Diffusion of Innovations. There are

a number of key elements in Roger’s (2003) diffusion

theory, beginning with an idea and its perceived desirability

or undesirability and followed by the idea being

communicated by the media, interpersonal channels, or

other vehicles (Rogers 2003). Next, there is a time element

required for the idea to move around to key opinion leaders

and finally the idea will be accepted by a social structure,

leaders or society

(Rogers 2003). The three main types of innovation

decisions are: (1) optional innovation decisions, where

individuals make a decision to adopt or reject a new idea

independent of others members of their social system, (2)

collective innovation decisions where consensus is reached

among members of a social system to adopt or reject an

innovation or new idea, and (3) authority innovation

decisions where individuals with power, status or technical

expertise decide to adopt or reject an idea or innovation

(Rogers 2003). Rogers also includes a fourth category,

―contingent innovation-decisions‖ where choices to adopt or

reject are reconsidered after an initial decision to not adopt.

Whether or not an idea is adopted at a fast or slow rate in a

particular group is contingent upon decision-innovation

options and the outcome of each option results in

consequences for individuals, units, or social system

(Rogers 2003, p. 38).

There are four key elements in the diffusion of innovations.

First, there is innovation—an idea or practice that is new to

an individual or organization. There may be favorable or

unfavorable responses to the innovative idea and the

desirability of the idea is based on the relative advantage to

the group, its compatibility, complexity, trialability, and

observability (Table 2). The second key element in diffusing

new ideas is communication channel—the role of mass

media and how effective it is in communication of

knowledge about new ideas. Interpersonal peer channels are

influential in this the role of media and whether or not

individuals accept or reject new ideas. Other significant

factors are the qualities between two or more individuals

and how they respond to the communications about the new

idea.

Table 2. Intrinsic Characteristics to Adopt or Reject an Innovation (Rogers 2003)

Factor Definition

Relative

Advantage

The improvement of an innovation over the previous generation.

Compatibility The level of compatibility that an innovation has to be assimilated into an individual’s life.

Complexity or

Simplicity

If the innovation is perceived as complicated or difficult to use, an individual is unlikely to adopt it.

Trialability How easily an innovation may be experimented. If a user is able to test an innovation, the individual will

be more likely to adopt it.

Observability The extent that an innovation is visible to others. An innovation that is more visible will drive

communication among the individual’s peers and personal networks and will in turn create more positive

or negative reactions.

The third element identified by (Rogers 2003)is time—a key

part of the innovation decision process because of the time it

takes for a new idea to come to an individual or other unit. It

also takes time for knowledge to convey from one person to

another, time for decisions to be made to either adopt or

reject new ideas, and time to implement and confirm

decisions about the innovation. Finally, social systems are

important and the structure of those systems can facilitate or

impede diffusions of innovations. Rogers (2003) notes

various types of opinion leaders and change agents operated

in social structures where they influenced the attitudes of

others. The professional change agents are focused on

producing desired outcomes and tried to influence client

behavior, but may not have been as successful as their peers

in the social structure.

Rogers (2003, p. 267)makes the observation that

―individuals in a social system do not all adopt an

innovation at the same time‖. He classifies individuals in

―adopter categories‖ based on when they first began using


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_______________________________________________________________________________________

an idea and identifies five adopter categories called ―ideal

types‖ based on ―abstractions from empirical investigations‖

(Rogers 2003, p. 282). Based on Rogers’ analysis, it should

be possible to understand the ideal types among

professionals and to design approaches for each type in

order to establish frameworks that increase

knowledgeregarding TA.Rogers defines five stages of the

adoption process categories (Table 2).

Table 3. Five Stages of the Adoption Process (Rogers 2003)

Stage Definition

Knowledge The individual is first exposed to an innovation but lacks information about the innovation.

During this stage of the process the individual has not been inspired to find more information

about the innovation.

Persuasion The individual is interested in the innovation and actively seeks information/detail about the

innovation.

Decision The individual takes the concept of the change and weighs the advantages/disadvantages of

using the innovation and decides whether to adopt or reject the innovation. Due to the

individualistic nature of this stage Rogers notes that it is the most difficult stage to acquire

empirical evidence.

Implementation The individual employs the innovation to a varying degree depending on the situation. During

this stage the individual determines the usefulness of the innovation and may search for further

information about it.

Confirmation The person finalises his/her decision to continue using the innovation. This stage is both

intrapersonal and interpersonal; confirmation the group has made the right decision.

These definitions are significant when determining how to

prepare individuals and communities for new ideas in the

form of technologies and innovations. Rogers believes ideas

and innovations are diffused by organizations and social

systems through opinion leaders and change agents with

defined roles who support or block adoption of the new

ideas (Rogers 2003). He observes that innovators who are

the most technologically ready are often not connected to

social networks, but early adopters and the early majority

are connected.

In discussing the role of opinion leaders in the adoption of

new ideas, Rogers indicates that new ideas are adopted by

social units or social systems where leaders perform key

roles in introducing new ideas. He shows that opinion

leaders function in ―diffusion networks‖ that are systems of

communications and dictate ―the degree to which an

individual is able informally to influence other individuals’

attitudes or overt behavior in a desired way with relative

frequency‖ (Rogers 2003, p. 300). Rogers underlines the

importance of understanding how to overcome the barriers

of getting new ideas adopted and how the absence of local

input can delay adoption of innovations. He emphasizes that

authority figures, followers, and change agents promote

change through spontaneous or the planned spread of new

ideas. Rogers views are summarized in his observation,

―Getting a new idea adopted, even when it has obvious

advantages, is difficult‖ (Rogers 2003, p. 1).

There are many characteristics cited by Rogers (2003) that

are important for explaining how ideas spread through

cultures. Opinion leadership is a significant category in

diffusion theory where opinion leaders have access to

external communications and as a result of travel have

access to mass media, exposure to change agents and

interface with different groups of professionals. The opinion

leaders are accessible to interpersonal networks where they

participate socially and have higher economic status than do

followers. Opinion leaders will generally adopt new ideas

before followers and are innovative even if they are not

innovators. The opinion leaders will reflect the norms of

their social systems and will be part of organizations used to

diffuse innovations (Rogers 2003).

The characteristics for opinion leaders are relevant to

professionals who incorporate the characteristics of well-

traveled cosmopolites with exposure to mass media and

access to external communications. Opinion leaders are

individuals who are part of networks that can diffuse

innovations and ideas rapidly. Rogers’ diffusion theory

should have significant application to diffusion of new

technologies among traditional business professional groups

and new and emerging technology-related occupations.


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A New Integrated Model: Readiness-Acceptance-

Diffusion (RAD)

TAM theorizes that the effects of external variables on

intention to use are mediated by perceived usefulness and

perceived ease of use. According to TAM, perceived

usefulness is also influenced by perceived ease of use

because, other things being equal, the easier the system is to

use the more useful it can be (Venkatesh & Davis 2000).

Since perceived usefulness is such a fundamental driver of

usage intentions, it is important to understand the

determinants of this construct and how their influence

changes over time with increasing experience using the

system. Perceived ease of use, TAM's other direct

determinant of intention, has exhibited a less consistent

effect on intention across studies. Whereas some research

has been done to model the determinants of perceived ease

of use (Venkatesh &Davis 1996), the determinants of

perceived usefulness have been relatively overlooked. A

better understanding of the determinants of perceived

usefulness would enable us to redesign organizational

interventions that would increase user acceptance and usage

of a new technology. Therefore, this study proposes a model

that extends TAM to include additional key determinants of

TAM's perceived usefulness and usage intention constructs,

and to add a combination of determinants into this model

that could be benefit to the model.

The technology acceptance models used to create the

alternative integrated model are summarized in Table 4

below.

Table 2. The Technology Acceptance Theories Used to Create the RAD Model

Name of Model Level of

Analysis

Dependent

Variables

Independent Variables Authors

Innovation

Diffusion

Theory (IDT)

Group,

Firm,

Industry,

Society

Implementation

Success or

Technology

Adoption

Relative advantage, ease of use,

visibility, result provability, image and

compatibility

(Rogers 2003)

Technology

Readiness (TR)

Individual Technology-

related

attitudes and

behaviors

Optimism, Innovativeness,

Discomfort, Insecurity

(Parasuraman

2000,

Parasuraman &

Colby 2001)

Unified Theory

of Acceptance

and Use of

Technology

(UTAUT)

Individual Behavioral

Intention, Use

Behavior

Performance expectancy, Effort

Expectancy, Social Influence,

Facilitating Condition, Gender, Age,

Experience, Voluntariness of Use

(Venkatesh et al.

2003)

The present study set out to integrate the fragmented theory

and research on individual acceptance of information

technology into a integrated theoretical model that captures

the essential elements of previously established models.

First, we identify and discuss the previous specific models

of the determinants of intention and usage of technology.

Second, these models are compared. Third, conceptual and

empirical similarities across models (Table 4) are used to

formulate the Readiness-Acceptance-Diffusion (RAD)

model (Figure 1).

RAD is a alternative model that synthesizes what is known

and provides a foundation to guide future research in this

area. By encompassing the combined explanatory power of

the individual models and key moderating influences, RAD

advances cumulative theory while retaining a parsimonious

structure. From a theoretical perspective, RAD also provides

a refined view of how the determinants of intention and

behavior evolve over time. It is important to emphasize that

most of the key relationships in the model are

moderated.According to Lam, Chiang & Parasuraman

(2008), the four TR constructs have distinct effects on

technology acceptance, suggesting that it may be suboptimal

to use the sum of the four constructs’ scores (the TRI) for

behavioral prediction or explanatory purposes or treat them

as reflective indicators of a second-order construct.

RAD underscores this point and highlights the importance

of contextual analysis in developing strategies for

technology implementation within organizations. While

each of the existing models in the domain is quite successful

in predicting technology usage behavior, it is only when one

considers the complex range of potential moderating

influences that a more complete picture of the dynamic


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_______________________________________________________________________________________

nature of individual perceptions about technology begins to

emerge. Despite the ability of the previous models to predict

intention and usage, theoretical perspectives on individual

acceptance are notably weak in providing prescriptive

guidance to designers (Venkatesh et al. 2003).

Although the RAD model has not been empirically tested to

date, the models on which it is based provide strong

theoretical support for RAD, which posits determinants of

intention to use technology. Moderating influences are

confirmed as integrated features of the RAD model.

While the variance explained by RAD is quite high for

behavioral research, further work should attempt to identify

and test additional boundary conditions of the model in an

attempt to provide an even richer understanding of

technology adoption and usage behavior. This might take

the form of additional theoretically motivated moderating

influences, different technologies, different user groups, or

other organizational contexts. Results from such studies will

have the important benefit of enhancing the overall

generalizability of RAD and/or extending the existing work

to account for additional variance in behavior. Specifically,

given the extensive moderating influences examined here, a

research study should examine the generalizability of these

findings with significant representation in each determinant.

One of the most important directions for future research is to

tie this mature stream of research into other established

streams of work. The integrated model presented here might

inform further inquiry into the short- and long-term effects

of information technology acceptance on job-related

outcomes such as productivity, job satisfaction,

organizational commitment, and other performance-oriented

constructs. Future research should study the degree to which

systems perceived as successful from a technology adoption

perspective are considered a success from an organizational

perspective.

CONCLUSION

Technology acceptance models generally focus on the

specific characteristics of the context, the individual, and the

innovation to predict future use. Whereas much research has

been done in the past 35 years about the processes

individuals go through to adopt and adapt to an innovation,

the constant bombardment of new information technologies

makes understanding the hows and whys of user technology

adoption a particularly pressing issue now and in the future.

In the study of informationtechnology implementations in

organizations, there has been a proliferationof competing

explanatorymodels of individualacceptance of

informationtechnology. Comparisons and contrasts of

theories of technology acceptance of scholars are presented

to identify major areas of agreement and areas of

differences.The present study advances individual

acceptance research by integrating theoretical perspectives

common in the literatureand incorporatingmoderators to

account for dynamic influences including contributor and

inhibitor determinants, organizational context,user

experience, and demographic characteristics.

Technology and innovations could change or alter cultures

and society for better or worse, depending on the

management of their introduction to society. This should

encourage business professionals, educators, and

policymakers to take great care with introducing innovations

and technologies into societies and cultures in order to

promote or avoid consequences that could last for

generations.

Future research on technology acceptance may examine the

consequences of technology to create a holistic

understanding of how technological changes influence the

organization and the individual. Whereas technology

acceptance may be viewed in terms of ramp-up time, or how

much time is lost in the learning of technology, researchers

should also be looking at how technologicalchanges modify

individuals’ views of technology. Finally, thejoint modeling

method that we use in this study could be useful for other

studies that involve bothadoption and usage of a technology.


_______________________________________________________________________________________________


_______________________________________________________________________________________

Figure 1. The Estimation of Alternative Integrated Technology Acceptance Model (Readiness-Acceptance

and Diffusion/RAD Model)

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Proposing a Comprehensive Meta-model for Technology Acceptance

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